Identification of angiotensin converting enzyme and dipeptidyl peptidase-IV inhibitory peptides derived from oilseed proteins using two integrated bioinformatic approaches

Abstract

This is a repository copy of Identification of angiotensin converting enzyme and dipeptidyl peptidase-IV inhibitory peptides derived from oilseed proteins using two integrated bioinformatic approaches.

Prediction and analysis of antimicrobial peptides from rapeseed protein using in silico approach

“…Fortunately, with the rapid development of bioinformatics, intelligent prediction based on computational methods has become an effective way to identify bioactive peptides in proteins and provide guidance on in vitro enzymatic hydrolysis for the production of peptides (Tu et al, 2018). Bioinformatics approach has been demonstrated to be useful and effective in the exploration of proteins-derived bioactive peptides, including angiotensin-I converting enzyme (ACE) inhibitory peptides, antioxidant peptides, and dipeptidyl peptidase IV (DPP IV) inhibitory peptides (Han et al, 2019;Tejano et al, 2019;Yu et al, 2018). Moreover, AMPs have been predicted by in silico approach from various proteins such as milk protein and tuber storage proteins (Dziuba & Dziuba, 2014;Ibrahim et al, 2019).…”

Optimization of Sorghum Kafirin Extraction Conditions and Identification of Potential Bioactive Peptides

“…DPP-IV inhibitors prevent GLP-1 breakdown and restore normal insulin secretion and probably the peptide generation from KAF can be a viable alternative for the inhibitor production without side effects. [35][36][37][38][39][40]42,43 On the other hand, ACE is an essential enzyme in the renin/angiotensin system that plays a vital role in controlling blood pressure and fluid balance. ACE raises blood pressure by catalyzing the reaction of the inactive decapeptide angiotensin-1 to the active vasoconstrictor angiotensin-2 causing high blood pressure and it is associated with increased heart failure, ischemic heart diseases, and coronary artery disease.…”

“…Wang et al 37 used the sequence of different proteins of animal origin (β-lactoglobulin, α-casein, β-casein, κ-casein, myoglobin) to predict possible peptides with DPP-IV inhibitory activity applying trypsin and pepsin. Han et al 38 carried out in silico hydrolysis of oilseed proteins such as sunflower and sesame, using subtilisin and pepsin, identifying peptides with DPP-IV inhibitory activity. In other studies, in silico the prediction of peptides with opioid, antioxidant, antimicrobial, and antithrombotic activity among others was achieved.…”

“…The chromatography system corresponds to a nanoACQUITY-UPLC system (Waters), has a Pre column Symmetry C18, 100 Å (5 lm • 20 mm), reverse-phase chromatographic column nanoACQUITY UPLC Peptide BEH C18, 130 Å (150 mm • 75 lm, particle size 1.7 lm) coupled with a nanoESI ionization for protein identification. The run of each sample was carried out under the following linear gradient in two phases of circumvention, one hy-drophilic (Water, A) with 0.1% formic acid and another hydrophobic (Acetonitrile, B) with 0.1% formic acid in a gradient elution analysis programmed as follows: 0 min, 3% (B); 0-1 min, 3% (B); 5-37 min, 9-80% (B); 37-38 min, 80-5% (B), 38 In silico analysis, peptide ranking, and bioactivity prediction The sequences of sorghum storage proteins identified by nanoLC-MS/MS were subjected to in silico analysis using the BIOPEP-UWM database to identify potential bioactive peptides and also the hydrolysis of these sequences in silico to produce using thermolysin (EC 3.4.24.27), subtilisin (EC 3.4.21.62), and chymotrypsin (EC 3.4.21.1) enzymes. The PeptideRanker (http://distilldeep.ucd.ie/PeptideRanker, in January 2020) was used to rank the predicted sequences of peptides according to bioactivity, and the occurrence frequency (A) of bioactive fragments with a potential activity in the protein sequences was calculated by the equation: A = a/N, a was the number of amino acid residue (aa) forming fragments with given activity in protein sequence, and N is the number of total aa of the protein identified by MS. 18…”

Safety of bovine milk derived extracellular vesicles used for delivery of RNA therapeutics in zebrafish and mice

“…The gel was sectioned into 8 slices according to molecular weight and analyzed by LC–MS/MS. Peptides were identified using a combination of Mascot, Sequest and X!Tandem UniProt bovine databases (Chen, Stupak, Williamson, Twine, & Li, ; Han, Maycock, Murray, & Boesch, ; Xie et al, ).…”